Machine learning Template->in Python

Importing the libraries

import numpy as np import matplotlib.pyplot as plt import pandas as pd

Importing the dataset

dataset = pd.read_csv('Data.csv') X = dataset.iloc[:, :-1].values y = dataset.iloc[:, 3].values

Taking care of missing data

from sklearn.preprocessing import Imputer imputer = Imputer(missing_values='NaN',strategy='mean',axis=0) imputer = imputer.fit(X[:, 1:3]) X[:, 1:3] = imputer.transform(X[:, 1:3]);

Encoding Categorical data

from sklearn.preprocessing import LabelEncoder,OneHotEncoder; labelEncoder_X = LabelEncoder() X[:,0]=labelEncoder_X.fit_transform(X[:,0]); oneHotEncoder = OneHotEncoder(categorical_features=[0]) X = oneHotEncoder.fit_transform(X).toarray(); labelEncoder_Y = LabelEncoder() y=labelEncoder_Y.fit_transform(y);

Splitting the data into training set and test set.

from sklearn.model_selection import train_test_split X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=0)

feature scaling

from sklearn.preprocessing import StandardScaler sc_X = StandardScaler(); X_train = sc_X.fit_transform(X_train); X_test = sc_X.fit(X_test);